LAUSR

When a sparse reconstruction (SR) space-time adaptive processing (STAP) strategy is employed for sea clutter suppression, the computational burden imposed by the spectrum reconstruction process is still a challenge. Hence, a low-complexity multiple-input multiple-output (MIMO) radar SR STAP strategy is developed via reducing model dimension and optimizing the SR scheme. On the one hand, the STAP echo model is projected into space and time domains owing to the uncoupled relationship; thus, the computation burden involved with large measurement matrix can be lessened by a low-dimension structure. Meanwhile, a coprime scheme is exerted to improve the degree of freedom (DOF) in submodels and ensure lower sampling consumption. On the other hand, to realize efficient spectral reconstruction, an improved SR algorithm is formulated via developing a prior matrix factor and a trade-off factor. Convergence speed of the algorithm is promoted by tuning the factors reasonably, and a clutter spectrum is quickly accessible with less iterations, so that adaptive filter can be constructed efficiently. According to the experiment results, high-efficiency spectral reconstruction is realized, and effective clutter suppression performance can be ensured.